Adaptive thread pool scaling and real-time thread monitoring

Master thesis (2022)

Authors

M.G.A. Janssen Electrical Engineering, Mathematics and Computer Science

Contributors

Jan S. Rellermeyer Data-Intensive Systems - EEMCS (mentor)
D.H.J. Epema Data-Intensive Systems - EEMCS (graduation committee member)
Z. Al-Ars Computer Engineering - EEMCS (graduation committee member)
Zaid Al-Ars Computer Engineering - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Martijn Janssen
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Published Date

15-08-2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Thread pools, integrated in programming languages, packages and dependencies are widely used by developers. Thread pools assume they are running alone on the system, which is not always the case. Previous research has shown that adapting thread pool size has been effective under specific conditions. In this research, scaling the thread pool with respect to CPU and network usage is examined. However, simpler metrics can achieve better results. Two solutions are provided for algorithmically scaling the thread pool, both with a different use-case in mind. Next to that an improved way to collect CPU and network metrics is provided, allowing for real-time thread-based measurements. The result of the scaling solution is improved performance, while also offering reduced energy usage. This research shows that when multiple thread pools are running on the same machine, performance and efficiency is improved.

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